Contactless interactive control technology based on switching filtering algorithm

2020 ◽  
pp. 014233122096613
Author(s):  
Yifan Fang ◽  
Lei Yu ◽  
Shumin Fei
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Point Interaction ◽  
Interactive System ◽  
Control Technology ◽  
Interactive Control ◽  
Filtering Algorithm ◽  
Time Performance ◽  
Interaction Control ◽  
Excellent Accuracy

In the large-screen interactive system with lidar sensor, due to the low accuracy of the lidar and the instability of the users’ gestures, the system’s recognition and tracking of gesture coordinates cannot be well obtained. Aiming at solving the problems of swaying and drifting gestures of the traditional filtering algorithm with a lidar sensor, this paper proposes a contactless interaction control technology based on switching filtering algorithm, which can realize non-contact high-precision multi-point interaction. The proposed algorithm first recognizes and extracts users’ gestures, and then the gestures are mapped to the screen position. Also, the mouse operation is simulated to realize operations such as selecting, sliding, and zooming in and out. Besides, the algorithm can effectively solve jitter and drift problems caused by scanning defects of radar and instability of the user gesture operations. Experimental results show that by applying the switching filtering algorithm to the contactless human-computer interaction system, the interactive trajectory becomes smoother and more stable compared with the traditional filtering algorithms. The proposed algorithm exhibits excellent accuracy and real-time performance, supporting efficient interaction with multiple people.

Contactless interactive control technology with a switching system based on Butterworth filter and modified strong tracking Kalman filter

2021 ◽  
pp. 095965182110033
Author(s):  
Yifan Fang ◽  
Zhong Chen ◽  
Lei Yu ◽  
Shumin Fei
Keyword(s):  
Kalman Filter ◽  
Human Computer Interaction ◽  
Three Dimensional ◽  
Interactive System ◽  
Interactive Control ◽  
Filtering Algorithm ◽  
Interaction Field ◽  
Time Interaction ◽  
Processing Step ◽  
Computer Interaction

In the human–computer interaction field, a contactless interaction with large screens through gestures is very representative, and the recognition and filtering of gesture images are very important tasks. Aiming at solving the problems of interference and positioning drift of three-dimensional lidar sensors, this article proposes a contactless interactive control system based on switching filtering algorithm, which selects the Butterworth filtering and the modified strong tracking Kalman filter to be used in the filtering process. The proposed interactive system extracts and optimizes user gestures, maps the gestures to the screen, simulates mouse operations, and enables operations such as selection, sliding, zooming in and out, and others. This switching filtering algorithm effectively solves the accuracy problem of a single filtering algorithm and the rapidity of complex filtering algorithms in the signal processing step, and greatly improves the interaction accuracy without sacrificing too much processing time. The experimental results show that by applying the proposed switching filtering algorithm to a contactless human–computer interaction system, the system can achieve smooth gesture interaction. The proposed system can perform real-time interaction with multiple people, which fully verifies the effectiveness and superiority of the proposed algorithm.

Switching filtering method for interactive systems

2019 ◽  
Vol 233 (10) ◽  
pp. 1340-1350
Author(s):  
Junyi Hou ◽  
Lei Yu ◽  
Yifan Fang ◽  
Shumin Fei
Keyword(s):  
Real Time ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Interactive Systems ◽  
Interactive System ◽  
The Real ◽  
Noise Interference ◽  
Filtering Method ◽  
Time Performance ◽  
Filtering Effect

Aiming at the problem that the mixed noise interference caused by the mixed projection noise system is not accurate and the real-time performance is poor, this article proposes an adaptive system switching filtering method based on Bayesian estimation switching rules. The method chooses joint bilateral filtering and improved adaptive median filtering as the filtering subsystems and selects the sub-filtering system suitable for the noise by switching rules to achieve the purpose of effectively removing noise. The simulation experiment was carried out by the self-developed human–computer interactive projection image system platform. Through the subjective evaluation, objective evaluation, and running time comparison analysis, a better filtering effect was achieved, and the balance between the filtering precision and the real-time performance of the interactive system was well obtained. Therefore, the proposed method can be widely applied to various human–computer interactive image filtering systems.

Natural human–computer interaction control of multi operators in collaborative virtual maintenance based on optical human motion capture system

2016 ◽  
Vol 07 (02) ◽  
pp. 1650002
Author(s):  
Xiangyang Li ◽  
Zhili Zhang ◽  
Feng Liang ◽  
Qinhe Gao ◽  
Lilong Tan
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Motion Capture ◽  
Human Motion ◽  
Motion Capture System ◽  
Human Motion Capture ◽  
Interaction Control ◽  
Time Motion ◽  
Virtual Maintenance ◽  
Computer Interaction

Aiming at the human–computer interaction control (HCIC) requirements of multi operators in collaborative virtual maintenance (CVM), real-time motion capture and simulation drive of multi operators with optical human motion capture system (HMCS) is proposed. The detailed realization process of real-time motion capture and data drive for virtual operators in CVM environment is presented to actualize the natural and online interactive operations. In order to ensure the cooperative and orderly interactions of virtual operators with the input operations of actual operators, collaborative HCIC model is established according to specific planning, allocating and decision-making of different maintenance tasks as well as the human–computer interaction features and collaborative maintenance operation features among multi maintenance trainees in CVM process. Finally, results of the experimental implementation validate the effectiveness and practicability of proposed methods, models, strategies and mechanisms.

scholarly journals A Real-Time Garbage Truck Supervision and Data Statistics Method Based on Object Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xuanyu Zhang ◽  
Yining Gao ◽  
Guangyi Xiao ◽  
Bo Feng ◽  
Wenshu Chen
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Commercial Application ◽  
Processing Unit ◽  
Maintenance Costs ◽  
Time Performance ◽  
Data Statistics ◽  
Communication Equipment ◽  
Excellent Accuracy

Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.

scholarly journals Application of Machine Learning in Air Hockey Interactive Control System

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7233
Author(s):  
Ching-Lung Chang ◽  
Shuo-Tsung Chen ◽  
Chuan-Yu Chang ◽  
You-Chen Jhou
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Interactive System ◽  
System Response ◽  
Learning Technology ◽  
Chip Design ◽  
Interactive Control ◽  
The Real ◽  
System Response Time ◽  
Linear Slide

In recent years, chip design technology and AI (artificial intelligence) have made significant progress. This forces all of fields to investigate how to increase the competitiveness of products with machine learning technology. In this work, we mainly use deep learning coupled with motor control to realize the real-time interactive system of air hockey, and to verify the feasibility of machine learning in the real-time interactive system. In particular, we use the convolutional neural network YOLO (“you only look once”) to capture the hockey current position. At the same time, the law of reflection and neural networking are applied to predict the end position of the puck Based on the predicted location, the system will control the stepping motor to move the linear slide to realize the real-time interactive air hockey system. Finally, we discuss and verify the accuracy of the prediction of the puck end position and improve the system response time to meet the system requirements.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

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